Yield of tuberculin screening among injection drug users.
Bibliographic record
Abstract
BACKGROUND: Regardless of their HIV status, injection drug users (IDUs) are at increased risk of developing active tuberculosis (TB) if they have latent TB infection (LTBI). We quantified the prevalence and predictors of LTBI and level of adherence to medical evaluation in a population of IDUs in Montreal. METHODS: Participants were recruited from an ongoing dynamic cohort of IDUs followed for HIV seroconversion risk behaviour. Subjects with a tuberculin skin test (TST) of > or =5 mm were referred to designated TB clinics for medical evaluation. A financial incentive was provided for TST readings. RESULTS: Of the 262 subjects tested, 246 (94%) returned for TST reading. The overall prevalence of positive TSTs was 22% (5% in HIV-positive, 28% in HIV-negative participants). Older age at first injection drug use (OR per 10 year increase in age 1.4, 95%CI 1.2-1.8), duration of injection drug use (OR per 10 year increase 1.6, 9.5%CI 1.5-2.2) and negative HIV status (OR 11.2, 95%CI 3.2-4.0) were independent predictors of a positive TST. Nine per cent of all TST-positive participants completed LTBI treatment. CONCLUSION: TB screening activities with incentives can be successful in detecting TST-positive individuals, but better strategies are needed for medical follow-up in this high-risk group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".